Patient Reported Outcome Measures (PROMs) have arrived in sports and exercise medicine: Why do they matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clinicians and administrators have a professional obligation to contribute (OTC) to improvement of healthcare quality. At the same time, participation in embedded research poses risks to healthcare institutions. Disclosure of an institution’s sensitive information could endanger relationships with patients and undermine its reputation. The existing ethical framework (EF) for learning healthcare systems (LHSs) does not address the conflict between the OTC and institutional interests. Ethical guidance and policy regulation are needed to create a safe environment for embedded research. In this article we analyse the EF for LHSs and the concept of professionalism. We suggest that the EF should be supplemented with an obligation to protect provider’s legitimate interests. We define legitimate interests as those that enable providers to discharge their primary duties. We argue that both the OTC and the obligation to protect legitimate interests are grounded in the concept of medical professionalism and can be understood as a matter of contract between a democratic society and medical professionals. The proposed supplemented EF can be implemented into a regulatory system in three different ways: the <i>self-regulating</i>: where providers decide themselves how to balance the ethical claims, the centralised: where a governmental institution decides the right balance between providers’ interests and interests of a health system; and the <i>mediating</i>: where medical professionals, the state and patients negotiate their interests. Our article contributes to the discussion on ethical relevance of providers’ interests and the regulatory model for weighing opposite interests in LHSs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it